Method and system to bundle message over a network

ABSTRACT

The invention describes a method and system to optimize network bandwidth and obtain greater efficiency in transmission of messages/data in, a client-server network. The invention proposes the use of clustering of client requests and the data items in such a manner so as to optimize the network transmission as well as reduce the cost of processing involved in sending and picking/pruning the data items at server and client end respectively.

FIELD OF THE INVENTION

[0001] The present invention relates to the field of information dissemination over a computer network. More particularly the invention relates to client server architecture in a computer network where clients seek information items from the server by generating request messages and receiving data messages.

BACKGROUND OF THE INVENTION

[0002] Modem computer networks often involve systems/agents/servers that are required to maintain (have) a large database of information (Items). For example, yahoo.com provides news, stock market quotes, sports information, multimedia content etc involving a multitude of large databases. The databases are used to serve requests for subsets of items of information from various client systems (Information seekers/Seekers).

[0003] The most obvious manner of fulfilling the requests is by sending the requested subsets of items to each individual client. This approach however may burden the computational resources of the server as well as the network resources. Alternatively, the server could broadcast all items to all information seekers and the individual recipients would then extract the information required by it. This solution results in inefficient utilization of the network bandwidth while at the same time burdening each recipient with the task of searching through an enormous amount of information. One manner of resolving the contradicting requirements on computational costs at the server as well as at the client system is to group items that are requested by groups of clients and then furnish the set of responses to the corresponding groups of clients. In this manner, a tradeoff between the contradicting requirements can be achieved that optimizes a global objective.

[0004] There are various types of costs involved in servicing the requests from clients. The server will incur some cost each time it sends an information bundle across to the clients.

[0005] A given client may not receive exact number of items that it has requested; it may receive more/less number of items than that the requested number. There is a cost associated with each item that a client did not receive. Also, there is a cost associated with each item it received and did not request for, because the client has an additional burden to prune such extra information.

[0006] U.S. Pat. No. 5,805,823 provides a system and method for optimal multiplexed message aggregation between client applications in a client-server network. This invention provides for a message architecture that multiplexes messages to a client. This invention does a plain simple aggregation of messages and not of clients. Further the aggregation done is plain and simple and no optimisation technique is defined in order to save on computational resources.

[0007] U.S. patent Publication Ser. No. 2,002,020,124,101A1 relates to server side optimization of is content delivery to clients by selective in-advance delivery to enable performance optimization based on the current load of the server. This invention based on probabilistic measure delivers the content in advance to the clients. It does not take into consideration the actual requests by one or more clients.

[0008] U.S. patent Publication Ser. No. 20,010,027,494A1 bundles one or more messages destined for the same address or sub-address. The data packets are managed only for the same client and the computing devices being served by it. The bundling is done based on the user-defined time limit or the packet size. The invention does not disclose any method wherein an optimisation between bundling the messages as against transmitting them alone is achieved.

[0009] U.S. Pat. No. 6,407,994 provides a system and method for bundling messages for transmission in a telecommunications network. This patent bundles one or more messages intended only for a particular client and does not take into consideration other clients having the requests for the same information item. Thereby though this patent reduces the bandwidth requirement, it does nothing to tackle the processing overhead involved at the server or at the client end.

SUMMARY OF THE INVENTION

[0010] The object of the present invention is to minimize the overall cost of satisfying clients' requests by simultaneously clustering the clients and their requests. The optimization covers computational costs at both the server and the client.

[0011] To achieve the said objective the proposed invention first formulates the various costs involved in serving requests from various clients. The invention then applies a method and system for simultaneously clustering clients and items. The clustering is carried-out over a number of requests and clients over a range of configurable pre-defined values and calculates the costs involved for each chosen cluster number value. The invention uses a fuzzy clustering algorithm for simultaneously clustering the clients and items so as to minimize the overall cost of satisfying the clients' requests.

BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The novel features believed characteristic of the invention are set forth in the appended claims. The invention itself, however, as well as a preferred mode of use, further objectives and advantages thereof, will best be understood by reference to the following detailed description of an illustrative preferred embodiment when read in conjunction with the accompanying drawings, wherein:

[0013]FIG. 1 shows a basic network structure comprising of the servers (storing items) and the clients.

[0014]FIG. 2 depicts the internal structure of a generic computing system on which the invention might be practiced by using them as a server as well as a client.

[0015]FIG. 3 is a flowchart for the method described herein.

[0016]FIG. 4 is a flowchart for the process of simultaneously clustering items and clients for a given number of clusters.

DETAILED DESCRIPTION

[0017]FIG. 1 shows a general client-server network on which the invention might be practiced. It consists of one or more servers (1.6) connected to a network (1.1). The network and scope covers all types of networks such as Local Area Network, Internet and the like. Also present on the network are different clients (1.2, 1.3, 1.4, 1.5). The interconnection between different clients on the network is by any known communication means such as wired links, radio links or by infrared transmissions. The networking topology covers all known topologies such as star, linear, ring or a combination of any of these. The clients and the server communicate using any of the known communication protocols such as TCP/IP or Ethernet. The number of servers and the clients is not limited and the data could reside either on one server or could be distributed over a number of servers. The server (1.6) acts as the information store and clients are seekers of information sending requests to the server for information items contained therein.

[0018] The clients (1.2, 1.3, 1.4, 1.5) comprise be electronic devices such as personal computers, mobile phones, interactive televisions and the like, operated by humans or software agents operating on behalf of individuals or organizations.

[0019]FIG. 2 shows a block diagram of a general computing system (2.1) on which the invention might be practiced. The computer system (2.1) consists of various subsystems interconnected with the help of a system bus (2.2). The microprocessor (2.3) communicates and controls the functioning of other subsystems. The microprocessor (2.3) also acts as the control unit operating in conjunction with memory (2.4) to perform operations as defined by the stored instructions. In a general computer system the control module is any commercially available processor of which ×86 processors from Intel and 680×0 series from Motorola are examples. The computing system could be a single processor system or may use two or more processors on a single system or over a network. This control module also controls the functioning of the other components of the computing system (not shown). Control module (2.3) accesses said memory (2.4) through system bus (2.2) that interconnects the various parts of the computing device. The control module executes a program called the operating system for the basic functioning of the computer system. Some examples of operating systems are UNIX, WINDOWS and DOS. These operating systems allocate the computer system resources to various programs and facilitate the interaction of users with the system. Memory (2.4) supports the microprocessor in its functioning by storing instructions and data required for program execution. Examples of memory are random access memory devices such as dynamic random access memory (DRAM) or static memory (SRAM). Storage Device (2.5) is used to hold the data and instructions permanent in nature such as the operating system and other programs. Video Interface (2.6) is used as an interface between the system bus and the display device (2.7), which is generally a video display unit such as a monitor. The network interface (2.8) is used to connect the computer with other computers on a network which can be either a Local Area Network (LAN) or a Wide Area Network (WAN) or any other type of computer network, through wired or wireless means. This networking interface can also be used to connect to the Internet. The computer system might also contain a sound card (2.9). The system is connected to various input devices like keyboard (2.11) and mouse (2.12) and output devices like printer (2.13), through an input/output Interface (2.10). Various configurations of these subsystems are possible. It should also be noted that a system implementing the present invention might use less or more number of the subsystems than described above.

[0020] In the preferred embodiment of the invention, the instructions are stored on the storage device (2.5) in the form of a computer program. This program contains coded instructions for different algorithms described herein the specification. On running the program, the instructions are transferred to the memory (2.4) and the microprocessor (2.3) executes the instructions. The system can be manually controlled by giving instructions through means of input devices such as keyboard (2.11) and mouse (2.12). Instructions, whether from the program or from the user input reside in the memory (2.4) and are subsequently acted upon by the microprocessor (2.3). It should be understood that the invention is not limited to any particular hardware comprising the computer system or the software running on it.

[0021] Those of ordinary skill in the art will appreciate that the various means for generating service requests by the clients and their processing by the server are computer programs. These programs are capable of existing in an embedded form within the hardware of the system or may be embodied on various computer readable media. The computer readable media may take the form of coded formats that are decoded for actual use in a particular information processing system. Computer program means or a computer program in the present context mean any expression, in any language, code, or notation, of a set of instructions intended to cause a system having information processing capability to perform the particular function either directly or after performing either or both of the following:

[0022] a) conversion to another language, code or notation

[0023] b) reproduction in a different material form.

[0024] The depicted example in FIG. 2 is not meant to imply architectural limitations and the configuration of the incorporating device of the said means may vary depending on the implementation. Any kind of computer system or other apparatus adapted for carrying out the means described herein can be employed for practicing the invention. A typical combination of hardware and software is a general purpose computer system with a computer program that when loaded and executed, controls the computer system such that it carries out the means described herein Other examples of the incorporating device that may be used are notebook computers or hand held computers, PDAs etc.

[0025] The problem sought to be solved by the instant invention maybe defined as follows:

[0026] Let r₁, . . . , r_(N) be N information/message items and s₁, . . . , s_(M) be M clients. Let $a_{i\quad j} = \left\{ \begin{matrix} {1,} & {{{if}\quad s_{j}\quad {seeks}\quad r_{i}},{and}} \\ {0,} & {{otherwise}.} \end{matrix} \right.$

[0027] Let R₁, . . . , and R_(K) be sets of subsets of items that are sent to subsets S₁, . . . , and S_(K), of clients, respectively. Let R₁, . . . , and R_(K) be represented by N-dimensional binary column vectors and S₁, . . . , and S_(K) also by M-dimensional binary column vectors. Also, let R=[r_(ij)]=[R₁, . . . , R_(K)] and S=[s_(ij)]=[S₁, . . . , S_(K)] represent the corresponding matrices. That is, $\begin{matrix} {r_{i\quad j} = \left\{ \begin{matrix} {1,} & {{{if}\quad r_{i}\quad {is}\quad {in}\quad j\quad {th}\quad {cluster}},{and}} \\ {0,} & {{otherwise}.} \end{matrix} \right.} \\ {s_{i\quad j} = \left\{ \begin{matrix} {1,} & {{{if}\quad s_{i}\quad {is}\quad {in}\quad j\quad {th}\quad {cluster}},{and}} \\ {0,} & {{otherwise}.} \end{matrix} \right.} \end{matrix}$

[0028] Then, the cost at the server is proportional to K and the cost at a client depends on the number of extra items it received and the number of the items it requested but did not receive. Let T=[t_(ij)] where t_(ij) is the number of copies of r_(i) that s_(j) receives, $t_{\quad {i\quad j}} = {\sum\limits_{l = 1}^{K}\quad {r_{i\quad l}{s_{i\quad l}.}}}$

[0029] Then, the total number of items received by sj is Σ_(i) ^(t) ^(_(ij)) . Therefore, the total cost of sending R₁, . . . , R_(K) sets of items to S₁, . . . , S_(K) sets of clients is $\begin{matrix} {{\Phi \left( {R,S,K} \right)} = {{\alpha \quad K} + {\beta {\sum\limits_{j = 1}^{M}\quad {{t_{j}{\nabla\quad a_{j}}}}}}}} & (1) \end{matrix}$

[0030] where, a_(j)=[a_(1j), . . . , a_(Nj)] is the vector representing the items requested by s_(j), t_(j)=[t_(1j), . . . , t_(Nj)] is the vector representing the items received by s_(j), and |x∇y| represents the cardinality of the symmetric difference between vectors x and y. The problem then is to find R, S, and K such that φ(R, S, K) is minimized.

[0031] The solution to the above problem depends on the matrix A=[a_(ij)]. As a simple example, consider a case in which M clients, each seeking only one item, seek M distinct items (that is, M<N). Since all clients are identical from the optimization point of view, assume that the M items requested by the clients are grouped into K equal groups and each group of items is multicast to the corresponding set of clients that request the items in the group. Then, |t_(j)∇a_(j)|=M/K−1 the solution to the above optimization problem, result in K=M{square root}{square root over (β/α)}. One of the inferences from the above equation is that the items should be grouped and multicast to serve the requests only when βM²>α, i.e., the server processing cost is at least M times more important than that of clients.

[0032] The invention proposes to solve the aforementioned problem of optimization through clustering of clients and items. The basic steps involved are highlighted in FIG. 3. Firstly a range of values for the number of clusters is defined and K is set to the minimum value (3.1). Using the data of clients' requests (3.2), clients (seekers) and items are simultaneously clustered into ‘K’ clusters (3.3). The cost of processing with the resulting clusters of items and clients is then calculated (3.4). If the present cost is less than the minimum cost obtained previously (if any) (3.5) then present cost is stored as the minimum cost. Also, the present number of clusters is classified as optimum number of clusters. Next step increments K by 1 (3.6). If K becomes greater than the maximum value of clusters defined (3.7) then optimum number of clusters as stored is output and items and clients are clustered using this number (3.8). Steps 3.3 to 3.6 are repeated till maximum value for clusters is reached. The method of simultaneously clustering items and clients (3.3) is explained below in more detail:

[0033] Finding an optimal solution to the above stated problem, when A is an arbitrary binary matrix, is difficult. This invention proposes a clustering algorithm that approximately solves the above problem by finding a sub-optimal solution.

[0034] The solution operates by finding R and S that minimizes φ(R, S, K) for various values of K over a given range, and then selecting a K that minimizes the objective function. An optimization algorithm based on fuzzy set theory that optimizes φ(R, S, K) for a given K, denoted as φ(R, S) for simplicity is given below. For a given K, the solution optimizes $\begin{matrix} {{\Phi \left( {R,S} \right)} = {\sum\limits_{j = 1}^{M}\quad {{{t_{j}{\nabla\quad a_{j}}}}.}}} & (2) \end{matrix}$

[0035] Assume element s_(j) belongs to the l-th cluster with a fuzzy membership s_(jl) and r_(i) belongs to the l-th cluster with a fuzzy membership r_(il), where s_(jl) ε[0,1] and ril ε[0,1]. These fuzzy memberships are required to satisfy $\begin{matrix} {{{\sum\limits_{i = 1}^{K}\quad r_{i\quad l}} = 1},{and}} & (3) \\ {{\sum\limits_{j = 1}^{K}\quad s_{j\quad l}} = 1.} & (4) \end{matrix}$

[0036] To achieve this new objective function required to be minimized is:, $\begin{matrix} \begin{matrix} {{\Phi \left( {R,S} \right)} = {{\sum\limits_{j = 1}^{M}\quad {{t_{j}{\nabla\quad a_{j}}}}} + {\rho \left( {R,S} \right)} +}} \\ {{{\underset{i = 1}{\overset{N}{\sum\quad}}\quad {\eta_{i}\left( {{\sum\limits_{l = 1}^{K}\quad r_{i\quad l}} - 1} \right)}} + {\underset{j = 1}{\overset{M}{\sum\quad}}{\mu_{j}\left( {{\sum\limits_{l = 1}^{K}\quad s_{j\quad l}} - 1} \right)}}}} \end{matrix} & (5) \end{matrix}$

[0037] where, ρ(R, S) is a regularization function that helps in specifying the degree of fuzziness, and η_(i) and μ_(j) are the Lagrange's multipliers corresponding to conditions (3) and (4) respectively. The fuzzy symmetric difference between t_(j) and a_(j) is computed as ${{t_{j}{\nabla\quad a_{j}}}} = {\sum\limits_{i = 1}^{N}{{{t_{i\quad j} - a_{\quad {i\quad j}}}}.}}$

[0038] One of the examples of ρ(R, S) is $\begin{matrix} {{\rho \left( {R,S} \right)} = {{\lambda_{r}{\sum\limits_{i = 1}^{N}{\sum\limits_{l = 1}^{K}\quad {r_{i\quad l}{\log \left( r_{i\quad l} \right)}}}}} + {\lambda_{s}{\sum\limits_{j = 1}^{M}{\sum\limits_{l = 1}^{K}\quad {s_{j\quad l}{\log \left( s_{j\quad l} \right)}}}}}}} & (6) \end{matrix}$

[0039] It is to be noted that individual terms in ρ(R, S) maximize when s_(jl) and r_(il) equals to either 1 or 0. λ_(r) and λ_(s) are the weighting parameters that specify the degree of fuzziness. Let u_(ij)=sign (t_(ij)−a_(ij)) where, ${{sign}(x)} = \left\{ \begin{matrix} 1 & {{{{if}\quad x} > 0},} \\ 0 & {{{{{if}\quad x} = 0},}\quad} \\ {- 1} & {{otherwise}.} \end{matrix} \right.$

[0040] Then, ${{t_{j}{\nabla a_{j}}}} = {\sum\limits_{i = 1}^{N}\quad {{u_{i\quad j}\left( {t_{i\quad j} - a_{i\quad j}} \right)}.}}$

[0041] The necessary conditions for the optimality of φ with ρ(R,S) as given in (6) with respect to s_(jl) and r_(il) are given below: $\begin{matrix} {{\frac{\partial\Phi}{\partial s_{j\quad l}} = {{{\sum\limits_{i = 1}^{N}\quad {u_{i\quad j}r_{i\quad l}}} - {\lambda_{s}\left( {1 + {\log \left( s_{j\quad l} \right)}} \right)} + \mu_{j}} = {0\quad {and}}}},} & (7) \\ {{\frac{\partial\Phi}{\partial r_{i\quad l}} = {{{\sum\limits_{j = 1}^{M}\quad {u_{i\quad j}s_{j\quad l}}} - {\lambda_{r}\left( {1 + {\log \left( r_{i\quad l} \right)}} \right)} + \eta_{i}} = 0.}}\quad} & (8) \end{matrix}$

[0042] And, the necessary conditions with respect to ηi and μj are defined in equations (3) and (4). Solving for s_(jl) and r_(il) from equations (3), (4), (7) and (8), results in: $\begin{matrix} {{s_{j\quad l} = {\frac{\exp \left( {- \left( {\frac{{\hat{r}}_{j\quad l}}{\lambda_{s}} + 1} \right)} \right)}{\sum\limits_{m = 1}^{K}\quad {\exp \left( {- \left( {\frac{{\hat{r}}_{j\quad m}}{\lambda_{s}} + 1} \right)} \right)}}\quad {and}}},} & (9) \\ {r_{i\quad l} = \frac{\exp \left( {- \left( {\frac{{\hat{s}}_{i\quad l}}{\lambda_{r}} + 1} \right)} \right)}{\sum\limits_{m = 1}^{K}\quad {\exp \left( {- \left( {\frac{{\hat{s}}_{i\quad m}}{\lambda_{r}} + 1} \right)} \right)}}} & (10) \end{matrix}$

[0043] where, $\begin{matrix} {{\hat{r}}_{j\quad l} = {{\sum\limits_{i = 1}^{N}\quad {u_{i\quad j}r_{i\quad l}\quad {and}\quad {\hat{s}}_{i\quad l}}} = {\sum\limits_{j = 1}^{M}\quad {u_{i\quad j}{s_{j\quad l}.}}}}} & \quad \end{matrix}$

[0044] Picard iteration is used with (9) and (10) to optimize the objective function given in (5). Start with some initial random values for s_(jl) and r_(il) the values of s_(jl) and r_(il) are updated using (9) and (10) respectively at every iteration, until convergence or some termination condition is achieved. Finally the fuzzy memberships s_(jl) and r_(il) are defuzzied to obtain crisp clusters of clients and items. FIG. 4 shows the various steps of the algorithm, which are summarized as below:

[0045] Step 1. Form the matrix A=[a_(ij)] based on the requests made by various clients (4.1).

[0046] Step 2. Initialize s_(jl) and r_(il) randomly such that equations (3) and (4) are satisfied (4.2).

[0047] Step 3. Compute a new set of s_(jl), s′_(jl) using equation (9). (4.3)

[0048] Step 4. Compute a new set of r_(il), r_(il)′ using equation (10). (4.3)

[0049] Step 5. If ${{\sum\limits_{i,l}{{r_{i\quad l}^{\prime} - r_{i\quad l}}}} > \theta_{r}},{{{and}\quad {\sum\limits_{j,l}{{s_{j\quad l}^{\prime} - s_{j\quad l}}}}} > \theta_{s}}$

[0050] (4.4) then copy r′_(il) to r_(il), copy s′_(jl) to s_(jl), and go to Step 3. (4.5,4.6)

[0051] Step 6. Copy r′_(il) to r_(il), and copy s′_(jl) to s_(jl). (4.7)

[0052] Step 7. Defuzzify s_(jl) and r_(il). (4.7)

[0053] Step 8. End.

[0054] Defuzzification converts a vector of fuzzy memberships to a vector of binary values. In other words, it assigns items and clients to various clusters based on the fuzzy memberships. Suppose f=(f₁, . . . , f_(K)) represents the fuzzy membership of an item or a client in cluster C_(l), for l=1, . . . , K. The method given below defuzzifies f to obtain g=(g₁, . . . g_(K)) where g_(i) is binary for i=1, . . . , K. Let f′=max, (f−l), then the elements of g are obtained using the following equation: $g_{i} = \left\{ {{{\begin{matrix} {1,} & {{{if}\quad f_{i}} > {\gamma \quad f^{\prime}}} \\ {0,} & {otherwise} \end{matrix}\quad {for}\quad i} = 1},\quad \ldots \quad,K} \right.$

[0055] where γ is a constant less than 1.

[0056] Other Modifications:

[0057] The other form of regularization term, ρ(R, S) possible is: $\begin{matrix} {{\rho \left( {R,S} \right)} = {{\lambda_{r}{\sum\limits_{i = 1}^{N}\quad {\sum\limits_{l = 1}^{K}\quad r_{i\quad l}^{2}}}} + {\lambda_{s}{\sum\limits_{j = 1}^{M}\quad {\sum\limits_{l = 1}^{K}\quad s_{j\quad l}^{2}}}}}} & (10) \end{matrix}$

[0058] Using this in equation (5) the resultant update equations for s_(jl) and r_(il) are $\begin{matrix} {{r_{j\quad l} = {\frac{1}{2\quad \lambda_{s}}\left( {{\frac{1}{K}{\sum\limits_{m = 1}^{K}{\hat{s}}_{m}}} - {\hat{s}}_{l}} \right)}},{and}} & (11) \\ {s_{j\quad l} = {\frac{1}{2\quad \lambda_{s}}{\left( {{\frac{1}{K}{\sum\limits_{m = 1}^{K}{\hat{r}}_{m}}} - {\hat{r}}_{l}} \right)\quad.}}} & (12) \end{matrix}$

[0059] It will be apparent to those with ordinary skill in the art that the foregoing is merely illustrative and not intended to be exhaustive or limiting, having been presented by way of example only and that various modifications can be made within the scope of the above invention. The present invention can be realized in hardware, software or a combination of hardware and software. The modules as described in the invention could either be realized in a centralized manner, on one computer system could be spread across several interconnected computer systems. Any kind of computer system or other apparatus adapted for carrying out the methods described herein is suited. A typical combination of hardware and software could be a general purpose computer system with a computer program that, when loaded and executed, controls the computer system such that it carries out the methods described herein.

[0060] Accordingly, this invention is not to be considered limited to the specific examples chosen for purposes of disclosure, but rather to cover all changes and modifications, which do not constitute departures from the permissible scope of the present invention. The invention is therefore not limited by the description contained herein or by the drawings, but only by the claims. 

What is claimed is:
 1. A method for minimizing the cost of processing client requests in a client-server network, comprising the steps of: selecting a range of values for the number of clusters into which incoming client requests are to be clustered, simultaneously clustering requests and requesting clients for each selected value, calculating the cost of processing each cluster of requests and requesting clients, and determining the cluster combination that provides minimum cost of processing.
 2. The method as claimed in claim 1 further comprising the step of assigning the clusters of items to the respective clusters of clients.
 3. The method as claimed in claim 1, wherein the step of selecting a range of values for the number of clusters is a configurable manual process.
 4. The method as claimed in claim 1, wherein the step of clustering for each value in the chosen range for the number of clusters, comprises the steps of: randomly initializing the fuzzy membership values of requests and requesting clients, iteratively calculating the fuzzy membership values until the sum of changes of membership values is less than a configurable threshold value, and defuzzifying the fuzzy membership values to obtain actual clusters of items and clients.
 5. In a client-server network a system for minimizing the cost of servicing client requests comprising: a selection means for selecting a range of values for the number of clusters into which incoming client requests are to be clustered, a clustering means for simultaneously clustering requests and requesting clients for each selected value, a calculating means for establishing the total processing cost for each selected value, and a determining means for establishing the minimum cost of processing.
 6. The system as claimed in claim 5 further comprising a means for assigning the clusters of items to the respective clusters of clients.
 7. The system as claimed in claim 5, wherein the requesting clients are request generating computing devices connected to one or more computing devices acting as servers servicing requests made by the clients.
 8. The system as claimed in claim 5, wherein the requests include information, messages and data required by the clients.
 9. The system as claimed in claim 7, wherein the said means reside on one or a combination of server devices comprising: a system bus, a communications unit connected to the system bus, a memory including a set of instructions connected to the system bus, and a control unit executing the instructions in the memory.
 10. The system as claimed in claim 7, wherein the clients and server computing devices are interconnected through any suitable computer network including Ethernet, Internet, LAN, WAN, and MAN using any desired network topology including ring, bus and star.
 11. A computer program product comprising computer readable program code stored on a computer readable storage medium embodied therein for minimizing the total cost of processing client requests in a client-server network, comprising: computer readable program code means configured for selecting a range of values for the number of clusters into which client requests are clustered prior to processing, computer readable program code means configured for simultaneously clustering requests and clients for each selected value, computer readable program code means configured for calculating the total cost of processing for each selected value for the number of clusters, and computer readable program code means configured for determining the minimum cost of processing.
 12. The computer program product as claimed in claim 11 further comprising computer readable program code means configured for assigning the clusters of requests to the respective clusters of clients.
 13. The computer program product as claimed in claim 11, wherein the computer readable program code means of clustering for each value in the selected range for the number of clusters, comprise: computer readable program code means configured for randomly initializing the fuzzy membership values of items and clients, computer readable program code means configured for iteratively calculating the fuzzy membership values until the sum of changes of membership values is less than a configurable threshold value, and computer readable program code means configured for defuzzifying the fuzzy membership values to obtain actual clusters of items and clients. 